KANBAN 101
AT AGILE NEW ENGLAND
Presented by:
Code Genesys
Ajay Reddy
Jack Speranza
Official Licensed Material Copyright C...
Today’s Agenda
Improved Project / Release Planning
• Introduction & Background
• Planning with Little’s Law
• Project Plan...
Section 1 of 3
Background
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Project Planning Process
• Concept is Developed
• Project Parameters are Established
• Minimum Viable Product / Minimum
Ma...
The Moving Targets
The amount of work requested.
The amount of time needed.
The amount of resources allocated.
The level o...
The Challenge
Your mission, should you choose to accept
it…
• Establish a Reliable Delivery Date for
a Given Scope of Work...
The Headaches
Estimating Skills – some individuals /
teams are better than others at
estimating.
Work Item Variability – t...
Scrum Systems – Estimation & Velocity
• Estimation of hours or
story points is subjective
and imprecise
• Velocity measure...
Throughput Focus
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Throughput r...
Kanban Systems – Flow Based
Queuing system design plus granular tracking of work types and
workflow enables probabilistic ...
Lead Time Focus
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Traditional L...
Lead Time Histogram
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Section 2 of 3
Planning with Little’s Law
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | ...
Kanban / Scrumban as a Queuing System
Source: Dimitar Bakardzhiev
Official Licensed Material Copyright CodeGenesys, LLCtra...
Applying Little’s law
Departure rate approximately equals
arrival rate (λ).
No work items get lost and none
remain in the ...
The Basic Math
Use with care – there are many variables that
influence reliability of any forecast.
Best Use is to attain ...
The Math in Action
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Proposed P...
The Math in Action (cont.)
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Fo...
The Math in Action (cont.)
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Fo...
But Wait… What About the Z-curve?
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegen...
Accounting for
Uncertainty
Natural ebb & flow (associated with the
1st & 3rd legs of the z-curve)
Dark Matter (work expans...
Managing the
Uncertainty
• 1st & 3rd Legs of Z-curve
• Dark Matter
• Failure Load
• Other Risks?
Calculate and Manage Agai...
Putting it All Together
Monitoring & Managing Projects in Flight
Planned project (Release) lead time is the
sum of the cal...
What About Sprint Planning?
The techniques applied in Release Planning can also be employed
at the Sprint Planning level:
...
Section 2 of 3
Evolutions
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Why Risk is Relevant
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
• Higher...
The Result – Better Decisions
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys...
Back to Sprint Planning
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
Impro...
Sprint Planning (cont.)
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
# sto...
Modeling & Simulations
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
• Mode...
KANBAN 101 ON STEROIDS
training@codegenesys.com | @codegenesys
Learn More :: Kanban Kickstart
Start using Kanban
in Less T...
Advanced Training Opportunities
Scrumban Bootcamp (1 Day)
Kanban Foundations (2 Days)
Kanban at Scale (2 Days)
Advanced Pr...
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Kanban 101 - An Introduction to Planning with Little's Law

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Sharing this presentation from our monthly series through Agile New England. This is a high-level introduction to planning and forecasting. The talk behind the slides is really more informative. If you'd like to learn more, connect with me.

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Kanban 101 - An Introduction to Planning with Little's Law

  1. 1. KANBAN 101 AT AGILE NEW ENGLAND Presented by: Code Genesys Ajay Reddy Jack Speranza Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  2. 2. Today’s Agenda Improved Project / Release Planning • Introduction & Background • Planning with Little’s Law • Project Planning • Sprint Planning • Evolutions • Enhanced Risk Management (work selection & prioritization) • Modeling & Simulations Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  3. 3. Section 1 of 3 Background Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  4. 4. Project Planning Process • Concept is Developed • Project Parameters are Established • Minimum Viable Product / Minimum Marketable Features are Defined • Features are Prioritized • Estimates of Required Time and Resources are Established Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com
  5. 5. The Moving Targets The amount of work requested. The amount of time needed. The amount of resources allocated. The level of quality delivered. Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  6. 6. The Challenge Your mission, should you choose to accept it… • Establish a Reliable Delivery Date for a Given Scope of Work and Set of Resources (Team Members) • Establish a Reliable Alternate Date if Resources were Taken Away / Added or Scope of Work were Changed Is this truly possible??? Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  7. 7. The Headaches Estimating Skills – some individuals / teams are better than others at estimating. Work Item Variability – the development process has too many variables that influence “lead time.” Outliers are inevitable. Non-uniform Metrics – the most performance metrics are rarely apples to apples comparisons. Politics – estimates are invariably perceived as commitments. Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  8. 8. Scrum Systems – Estimation & Velocity • Estimation of hours or story points is subjective and imprecise • Velocity measurements are only relative and not comparable across multiple teams • Velocity is essentially a “throughput “-based measure. Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com
  9. 9. Throughput Focus Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Throughput represents the number of work items delivered in a given time period (such as a Sprint). -- a decent indicator of how well a team or organization is performing -- no clear indication of the amount of time required to complete work items…
  10. 10. Kanban Systems – Flow Based Queuing system design plus granular tracking of work types and workflow enables probabilistic forecasting. Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com Example of visualizing a single workflow through which all work types flow. Enables forecasting based upon objective measurements vs. subjective sizing. Example of visualizing multiple workflows that relate to specific classes of service (each of which may have different rates of flow). This system typically allows for more precise forecasting.
  11. 11. Lead Time Focus Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Traditional Labels – typically measured as the amount of time required to deliver a work item. -- measured from the date of commitment through delivery into the first infinite queue Alternative Labels – shifts focus to realization of business value. -- “flow time” + “delivery time” is the cycle relevant to our forecasting process.
  12. 12. Lead Time Histogram Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  13. 13. Section 2 of 3 Planning with Little’s Law Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  14. 14. Kanban / Scrumban as a Queuing System Source: Dimitar Bakardzhiev Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  15. 15. Applying Little’s law Departure rate approximately equals arrival rate (λ). No work items get lost and none remain in the system indefinitely. Little’s Law holds for queuing systems where: training@codegenesys.com Official Licensed Material Copyright CodeGenesys, LLC Can be applied at both iteration and release levels...
  16. 16. The Basic Math Use with care – there are many variables that influence reliability of any forecast. Best Use is to attain improved understandings & to influence expectations vs. making explicit commitments. Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  17. 17. The Math in Action Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Proposed Project Parameters • Web Application using familiar technologies and 2 stable Scrum Teams. • 50 Features defined. Historical Team Data • Average team size = 6 • Average # of Work-in-Progress (WIP) per team member = 2 • Historical density of 13.5 user stories per feature • Average lead time of .9 weeks per user story • Average throughput of 28 user stories per 2-week sprint (14 per week) Release Backlog = 50 Features x 13.5 Stories / Feature = 675 User Stories Proposed Project Scope
  18. 18. The Math in Action (cont.) Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Forecast Request # 1 • Can you complete the project as defined in 26 weeks using the resources allocated? Duration = Number of Stories x (Ave. Lead Time / Ave. WIP) = 675 x [.9/(12 resources x 2 items each)] = 25.3 weeks Forecasted Project Schedule Historical Team Data • Average team size = 6 • Average # of Work-in-Progress (WIP) per team member = 2 • Historical density of 13.5 user stories per feature • Average lead time of .9 weeks per user story • Average throughput of 28 user stories per 2-week sprint (14 per week)
  19. 19. The Math in Action (cont.) Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Forecast Request # 2 • We need to complete this project in 18 weeks. What additional resources do you need? Ave. WIP = Ave Lead Time x (Number of Stories / Duration) = .9 x (675 / 18) = 33.75 34 user stories / 2 WIP per resource = 17 resources Forecasted Resource Needs Historical Team Data • Average team size = 6 • Average # of Work-in-Progress (WIP) per team member = 2 • Historical density of 13.5 user stories per feature • Average lead time of .9 weeks per user story • Average throughput of 28 user stories per 2-week sprint (14 per week)
  20. 20. But Wait… What About the Z-curve? Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Little’s Law can only be reliably applied to work performed during the 2nd leg of the project / release z-curve…
  21. 21. Accounting for Uncertainty Natural ebb & flow (associated with the 1st & 3rd legs of the z-curve) Dark Matter (work expansion that comes from a better understanding of the work being undertaken) Failure Load (defects & technical debt, for example) When calculating Project (or Release) lead time, we need to account for uncertainty in terms of: Source: Dimitar Bakardzhiev Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  22. 22. Managing the Uncertainty • 1st & 3rd Legs of Z-curve • Dark Matter • Failure Load • Other Risks? Calculate and Manage Against a Project Buffer Accounting for: Have Team Focus on Median Lead Times Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Project Buffer = Z-curve coefficient x (1 + ave. failure load + ave. dark matter) x (# of stories / ave. throughput)
  23. 23. Putting it All Together Monitoring & Managing Projects in Flight Planned project (Release) lead time is the sum of the calculated project length and a project buffer. In our example: • 2nd Leg of Z-curve: 25.3 weeks • Project Buffer: 18.8 weeks • Total: 44.1 weeks Managing projects in flight requires evaluating two performance metrics -- the percentage of the project completed related to the percentage of the buffer consumed. Monitoring this relationship lets managers see and proactively address potential problems before they actually manifest themselves in more obvious ways. Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  24. 24. What About Sprint Planning? The techniques applied in Release Planning can also be employed at the Sprint Planning level: • Little's Law provides an additional check and balance on the correlation between velocity and actual throughput. • Teams that actively manage risk through embedded Risk Management techniques (such as Cost of Delay calculations and / or class of service mechanisms) are enabled to improve their story selection & prioritization capabilities, improving the likelihood of meeting their Sprint commitment. Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  25. 25. Section 2 of 3 Evolutions Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys
  26. 26. Why Risk is Relevant Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys • Higher Risk Means Greater Variability in Lead Times • greater variability means less predictability • Factors that Influence Risk • having to interact with other systems to deliver completed work • having to work with new systems or technologies • having to learn about new new business domains • and many more... • Quantifying, Tracking & Visualizing Risk • improves shared understandings of project health • enables early discovery and intervention
  27. 27. The Result – Better Decisions Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Which of these projects is more likely to be completed on time?
  28. 28. Back to Sprint Planning Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys Improving the Likelihood of Meeting Your Sprint Commitment • 70 User Stories remain in the Product Backlog. • Risk has been quantified on each on a crude scale: • High Risk Items: 20 User Stories • Medium Risk Items: 36 User Stories • Low Risk Items: 14 User Stories Historical Team Data • Average Lead Time for All Stories: .9 weeks • Average Lead Time for High Risk Stories: 1.6 weeks • Average Lead Time for Low Risk Stories: .4 weeks # stories = duration x (ave. WIP / ave. lead time) Proposed Sprint Backlog
  29. 29. Sprint Planning (cont.) Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys # stories = duration x (ave. WIP / ave. lead time) All Risk Stories (combined): # stories = 2 weeks x [(12 resources x 2 stories)/ .9 weeks] = 53.33 user stories High Risk Stories: # stories = 2 weeks x [(12 resources x 2 stories)/ 1.6 weeks] = 30 user stories Medium Risk Stories: # stories = 2 weeks x [(12 resources x 2 stories)/ 1 week] = 48 user stories Low Risk Stories: # stories = 2 weeks x [(12 resources x 2 stories)/ .4 weeks] = 120 user stories What a Difference Risk Makes When we don’t account for risk, 50 user stories may seem like an appropriate number of stories to select for the Sprint Backlog.
  30. 30. Modeling & Simulations Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys • Modeling is used when greater precision is required, or when we need to isolate and measure the impact of specific risk factors in a more granular fashion. • Modeling reality through repetitive simulations produces the rough equivalent of actual performance data • Past performance data (or its equivalent) improves our understanding of the probability of various outcomes that would otherwise remain elusive. • Elevated levels of uncertainty and risk means greater difficulty developing accurate forecasts. • Poor forecasting erodes trust and sense of predictability with non-technical leadership. • Trust & predictability are essential to sustained agility and high-performance.
  31. 31. KANBAN 101 ON STEROIDS training@codegenesys.com | @codegenesys Learn More :: Kanban Kickstart Start using Kanban in Less Than a Day! WHEN: Fourth Wednesday of the month 8:00 AM – 1:30PM $249 / person http://kanban-boston.eventbrite.com PRIVATE TRAINING AT YOUR LOCATION IS ALSO AVAILABLE (STARTING AT $1990 FOR UP TO 20 PEOPLE). WHY KANBAN?  Simple & Easy – Starts With What You’re Already Doing and Makes Those Practices More Effective  Improves Focus & Flexibility  Adds Ways to Identify & Reduce Wasted Work / Wasted Time  More Reliable Forecasting & Planning Capabilities  Increased Efficiency  Greater Sustainability What you walk away with:  Mastery of Core Principles & Practices  Analysis of Sources of Demand (where work is coming from) & Dissatisfaction (what business needs aren’t being met)  Value Stream Mapping of Current Process  Initial Kanban Board Design  3 Month Gold Level Subscription to ScrumDo.com plus Follow-up Mentoring
  32. 32. Advanced Training Opportunities Scrumban Bootcamp (1 Day) Kanban Foundations (2 Days) Kanban at Scale (2 Days) Advanced Practitioners More Details at: http://codegenesys.com/events Contact us: training@codegenesys.com

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